Most video observations are based on streams of video channels, which are surveyed by one or more employees of an organization, such as a police department. For example, employees of a police department may survey video to find a lost child in a county fair and/or a person of interest in a retail store. In another example, employees of a hospital may use video to monitor their patients.
Depending on its task, video observation requires different interpretations of recorded image sequences. For example, finding a lost child in a county fair may require only simple identification of the lost child in a recorded image, while finding a person of interest may require tracking and analyzing a person of interest on a video channel. More sophisticated observations may even require mathematical analysis of a stream of events and their results, which has led to the development of digital observation systems that help to process video streams more quickly.
However, current systems remain ineffective. For example, user-based systems may involve too few video streams, or those short in duration, which lead to valuable information being missed by user monitors. In other user-based systems, there are too many video streams, possibly running in parallel and spanning many hours, which may overwhelm user monitors and result in careless observations. While digital systems have helped increase video processing efficiency and accuracy, these digital systems also have significant limits, especially when advanced pattern recognition is required. For instance, while digital observation systems can adequately make simple identifications (e.g., of a speeding vehicle and its color and shape) and compute statistics (e.g., of a number of people moving in a monitored area), such digital observation systems may not perform some analysis as well as users (e.g., identify a person of interest through facial expressions).